title: Detection of cigarette smoke inhalations from respiratory signals using reduced feature set.
abstract: A combination of wearable Respiratory Inductive Plethysmograph and a hand-to-mouth Proximity Sensor (PS) can be used to monitor smoking habits and smoke exposure in cigarette smokers. In our previous work, detection of smoke inhalations was achieved by using a Support Vector Machine (SVM) classifier applied to raw sensor signals with 1503-element feature vectors. This study uses empirically-defined 27 features computed from the sensor signals to reduce the length of vectors. Further reduction in the length of the feature vectors was achieved by a forward feature selection algorithm, identifying from 2 to 16 features most critical for smoke inhalations detection. For individual detection models, the 1503-element feature vectors, 27-element feature vectors and reduced feature vectors resulted in F-scores of 90.1%, 68.7% and 94% respectively. For the group models, F-scores were 81.3%, 65% and 67% respectively. These results demonstrate feasibility of detecting smoke inhalations with a computed feature set, but suggest high individuality of breathing patterns associated with smoking.
authors.forename: Yogendra
authors.initials: Y
authors.lastname: Patil
authors.forename: Paulo
authors.initials: P
authors.lastname: Lopez-Meyer
authors.forename: Stephen
authors.initials: S
authors.lastname: Tiffany
authors.forename: Edward
authors.initials: E
authors.lastname: Sazonov
mesh_terms: D000328:Adult; D000465:Algorithms; D004867:Equipment Design; D005260:Female; D006801:Humans; D008297:Male; D010991:Plethysmography / Q000295:instrumentation* / Q000379:methods*; D012119:Respiration; D012815:Signal Processing, Computer-Assisted; D012907:Smoking; D060388:Support Vector Machine; D055815:Young Adult